Mechatronics最新文献

Tendon-driven continuum robots are conventionally modeled with either discrete or differential representations of their shapes, which neglect the physical design of the robot itself. As each segment of these robotic systems is usually realized by alternating compliant elements and rigid disks for tendon routing, these discontinuities cause non-negligible position and orientation errors. Although the factors that cause these curvature errors have often been identified in the mechanical behavior of the compliant element (usually made of superelastic alloys), tendon routing, and friction, no study available in the open literature gives a satisfactory explanation of these phenomena. In this article, a Finite Element (FE) model is proposed in conjunction with a bottom-up approach to study the physical behavior of this class of robots and ultimately to quantify the impact of these factors on the shape of a tendon-driven continuum robot. The model proved capable of approximating the experimental data with good accuracy, showing an average percentage error of 0.80% and a peak percentage error at the maximum curvature of the continuum robot of 1.30%, significantly smaller than the average error of 4.1% and peak error of 13.86% obtained with a conventional model.

Periodic non-destructive testing (NDT) of pipes and tanks is vital in industrial plants, such as Oil & Gas facilities, to proactively detect defects and corrosion before leaks and forced shutdowns occur. This paper presents a hybrid system, consisting of a UAV and a crawler, which enables detailed contact-based inspection of elevated pipes, in pursuit of eliminating the need for dangerous scaffolding and manual inspection to improve safety and reduce cost. Similar to avian animals, the UAV autonomously perches on the pipe to conserve energy. A small inspection crawling robot is carried by the UAV, and is subsequently released onto the pipe’s surface to inspect its health. The crawler uses magnetic wheels for agile mobility and houses an ultrasonic testing (UT) sensor to thoroughly scan the pipe and detect wall thinning, which is a precursor for leaks. Finally, the crawler re-docks with the UAV, which in turn detaches from the pipe to fly back home or inspect another pipe. The multi-robot system is designed for and tested on pipe diameters as small as 8 in.

This paper proposes a robust design of the time-varying internal model principle-based control (TV-IMPC) for tracking sophisticated references generated by linear time-varying (LTV) autonomous systems. The existing TV-IMPC design usually requires a complete knowledge of the plant I/O (input/output) model, leading to the lack of structural robustness. To tackle this issue, we, in this paper, design a gray-box extended state observer (ESO) to estimate and compensate unknown model uncertainties and external disturbances. By means of the ESO feedback, the plant model is kept as nominal, and hence the structural robustness is achieved for the time-varying internal model. It is shown that the proposed design has bounded ESO estimation errors, which can be further adjusted by modifying the corresponding control gains. To stabilize the ESO-based TV-IMPC, a time-varying stabilizer is developed by employing Linear Matrix Inequalities (LMIs). Extensive simulation and experimental studies are conducted on a direct-drive servo stage to validate the proposed robust TV-IMPC with ultra-precision tracking performance ($\sim 60\mathrm{nm}$ RMSE out of $\pm 80\mathrm{mm}$ stroke).

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

This study presents a 3D object detection technology for mobile platforms and its application. Rather than an innovative high-performance model, we proposed a “useable” model for the robot industry at the current technology stage by combining various techniques. To reduce computation time, a 2D region proposal was obtained using a RGB image-based CNN model. By applying the DBSCAN clustering technique to the point cloud corresponding to the 2D region proposal, a method of obtaining a 3D region proposal was proposed. This allowed for 3D object detection using an RGB image dataset, which has been widely researched, while reducing the computation load to a level suitable for use in mobile robots. Furthermore, the 3D object detection was integrated into a ROS 2-based mobile platform, which was used to perform pedestrian-safe avoidance tasks and elevator button operation tasks. The performance was confirmed through experiments.